Generative models, such as diffusion and flow-based models, have shown strong promise for robot policy learning by capturing complex and multimodal action distributions from demonstrations. However, policies trained solely with imitation learning often suffer from imperfect demonstrations and distributional shifts, whi...
Yu Li, Sheng-He Hu, Yu-Han Wang et al.· 0 citations
This work proposes a unified visuo-tactile-fusion grasping framework that integrates grasp generation, feasibility prediction, and adaptive refinement and introduces an efficient visuo-tactile representation that tightly fuses object geometry with tactile feedback by associating tactile signals with finger identities.
Xirui Liang, Jiaqi Liang, Jing-Kai Xu et al.· 0 citations
A latent motion prior module (\prior{}) is introduced that maps recent hand-action histories to a compact, history-conditioned latent prior and decodes continuous latent commands into executable high-dimensional hand targets and improves the policy with online residual RL in the same latent hand-action space.
Xinye Yang, Zhiyuan Ma, Hongze Yu et al.· 0 citations
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